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Statistical Inference in Context Specific Interaction Models for Contingency Tables

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  • Søren Højsgaard

Abstract

. Context specific interaction models is a class of interaction models for contingency tables in which interaction terms are allowed to vanish in specific contexts given by the levels of sets of variables. Such restrictions can entail conditional independencies which only hold for some values of the conditioning variables and allows also for irrelevance of some variables in specific contexts. A Markov property is established and so is an iterative proportional scaling algorithm for maximum likelihood estimation. Decomposition of the estimation problem is treated and model selection is discussed.

Suggested Citation

  • Søren Højsgaard, 2004. "Statistical Inference in Context Specific Interaction Models for Contingency Tables," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 143-158, March.
    • Handle: RePEc:bla:scjsta:v:31:y:2004:i:1:p:143-158
    DOI: 10.1111/j.1467-9469.2004.00378.x
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    Cited by:

    1. Colombi, R. & Forcina, A., 2014. "A class of smooth models satisfying marginal and context specific conditional independencies," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 75-85.
    2. Hara, Hisayuki & Sei, Tomonari & Takemura, Akimichi, 2012. "Hierarchical subspace models for contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 19-34, January.

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